Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

Article excerpt

In this article we provide insight into the BodyMedia FIT armband system-a wearable multisensor technology that continuously monitors physiological events related to energy expenditure for weight management using machinelearning and data-modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight loss. We describe several challenges that arise in applying machine-learning techniques to the health-care domain and present various solutions utilized in the armband system. We demonstrate how machine-learning and multisensor data-fusion techniques are critical to the system's success.

In the United States alone, approximately $2.6 trillion was spent on health care in 2010. It is well recognized that regular and accurate self-monitoring of physiological parameters and energy expenditure (calorie burn) can improve self-awareness of personal health by providing important feedback. Such awareness and tracking are prerequisites for cost-effective health management, illness reduction, health-conscious decision making, and long-term lifestyle changes.

There exists a wide spectrum of technologies available for monitoring physical activity, tracking energy expenditure, and managing weight. While many of these technologies provide some degree of accuracy, the most accurate among them, metabolic carts and calorimetry chambers, are bulky, expensive, and limited to laboratory and clinical use (Holdy 2004). In contrast, those that are small and inexpensive are, by and large, inaccurate.

At the high-accuracy end of the body monitor space is the doubly labeled water technique, a medical procedure that is guaranteed to give accurate measures of energy expenditure (Schoeller et al. 1986), but is very expensive and only gives readings for a 10- to 14-day period, making it impractical for continuous or short-term monitoring. At the less precise end of body monitor devices are several single-sensor (predominantly accelerometer-based) devices currently in the consumer market that are low cost and lightweight at the expense of accuracy (Beighle, Pangrazi, and Vincent 2001; Crouter et al. 2003).

We believe that a physiological monitoring device that provides estimates such as energy expenditure should be accurate, provide continuous user feedback, be user friendly, and be fully functional during all the activities of a user's daily life (free-living conditions). Moreover, the device should be cost-effective. The presented BodyMedia FIT armband system (BodyMedia 2011) achieves these goals. The effective integration of machinelearning methodologies and a multisensor technology used in a smart manner can rival medicalgrade equipment in terms of clinical accuracy, at the same time surpassing such equipment by collecting data in real time under free-living conditions.

The BodyMedia FIT system is able to provide accurate free-living energy expenditure estimates for two principal reasons-usage of machine-learning- based algorithms and multiple-sensor technology. The system employs state-of-the-art data modeling and machine-learning techniques to implement a data-centered process to estimate, rather than measure, most key physiological parameters. Multiple sensors operate concurrently to provide a real-time user activity context, which, in turn, provides a context-sensitive estimate of the users' physiological parameters.

This article will describe some of the challenges associated with estimating energy expenditure, engineering the BodyMedia FIT armband, applying machine-learning techniques used in developing the estimation algorithms, as well as the results of several studies assessing accuracy of the device and the practical utility of the device in a weight-loss scenario.

Background

Figure 1 shows the armband device (model MF). It is worn on the upper arm. …